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Zero-Shot Learning (ZSL) in video classification is a promising research direction, which aims to tackle the challenge from explosive growth of video categories.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Learning to detect unseen object classes by between-class attribute transfer
C. H Lampert, H. Nickisch, and S. Harmeling · 2009
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Zero-shot learning with semantic output codes
Mark Palatucci, Dean Pomerleau, Geoffrey E Hinton, and Tom M Mitchell · 2009
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Modeling temporal structure of decomposable motion segments for activity classification
Juan Carlos Niebles, Chih-Wei Chen, and Li Fei-Fei · 2010
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Hubs in space: Popular nearest neighbors in high-dimensional data
Miloš Radovanović, Alexandros Nanopoulos, and Mirjana Ivanović · 2010
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Consumer video understanding: A benchmark database and an evaluation of human and machine performance
Yu-Gang Jiang, Guangnan Ye, Shih-Fu Chang, Daniel Ellis, and Alexander C Loui · 2011
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Hmdb51: A large video database for human motion recognition
H. Kuehne, H. Jhuang, R. Stiefelhagen, and T. Serre · 2013
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Zero-shot learning through cross-modal transfer
Richard Socher, Milind Ganjoo, Christopher D Manning, and Andrew Ng · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Decorrelating semantic visual attributes by resisting the urge to share
Dinesh Jayaraman, Fei Sha, and Kristen Grauman · 2014
Cited alongside, same era.
Attribute-based classification for zero-shot visual object categorization
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2014
Cited alongside, same era.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Cited alongside, same era.
Zeroshot learning by convex combination of semantic embeddings
Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S Corrado, and Jeffrey Dean · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
Cited alongside, same era.
Two-stream convolutional networks for action recognition in videos
Synthesized classifiers for zero-shot learning
Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, and Fei Sha · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Generative adversarial text to image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
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Zero-shot learning via joint semantic similarity embedding
Z. Zhang and V. Saligrama · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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From zero-shot learning to conventional supervised classification: Unseen visual data synthesis
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Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Evaluation of output embeddings for fine-grained image classification
Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, and Bernt Schiele · 2015
Cited alongside, same era.
An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes and Philip Torr · 2015
Cited alongside, same era.
Zero-shot learning via semantic similarity embedding
Ziming Zhang and Venkatesh Saligrama · 2015
Cited alongside, same era.
Multi-cue zero-shot learning with strong supervision
Zeynep Akata, Mateusz Malinowski, Mario Fritz, and Bernt Schiele · 2016
Cited alongside, same era.
Yang Long, Li Liu, Ling Shao, Fumin Shen, Guiguang Ding, and Jungong Han · 2017
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Zero-shot learning-the good, the bad and the ugly
Yongqin Xian, Bernt Schiele, and Zeynep Akata · 2017
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Transductive zero-shot action recognition by word-vector embedding
Xun Xu, Timothy Hospedales, and Shaogang Gong · 2017
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Learning a deep embedding model for zero-shot learning
Li Zhang, Tao Xiang, and Shaogang Gong · 2017
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